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EMG-Based Feature Extraction and Classification for Prosthetic Hand Control

2021-07-01 20:19:03
Reza Bagherian Azhiri, Mohammad Esmaeili, Mehrdad Nourani
arXiv_AI
         
Abstract

In recent years, real-time control of prosthetic hands has gained a great deal of attention. In particular, real-time analysis of Electromyography (EMG) signals has several challenges to achieve an acceptable accuracy and execution delay. In this paper, we address some of these challenges by improving the accuracy in a shorter signal length. We first introduce a set of new feature extraction functions applying on each level of wavelet decomposition. Then, we propose a postprocessing approach to process the neural network outputs. The experimental results illustrate that the proposed method enhances the accuracy of real-time classification of EMG signals up to $95.5\%$ for $800$ msec signal length. The proposed postprocessing method achieves higher consistency compared with conventional majority voting and Bayesian fusion methods.

Abstract (translated)

URL

https://arxiv.org/abs/2107.00733

PDF

https://arxiv.org/pdf/2107.00733.pdf

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